Contactless Surgery Light Control based on 3D Gesture Recognition

Surgery lighting systems (SLS) aim to provide optimal lighting conditions for the surgeon in an operating room. To visually differentiate between only marginally different looking tissues, SLS offer settings for the level of illumination, color temperature and size of the illuminated field. By today’s standards, SLS are controlled with control panels that are situated at the lamp housings. The operation of the control panel is handled in an ergonomically unfavorable position with the hands above the operator’s head. On top, the region above the operator’s shoulder is considered as not sterile and causes a potential safety risk. To secure asepsis, it is required to extensively sterilize all equipment that the operator comes in contact with. In this paper, a contactless gesture control for a SLS is developed with the intention to offer the surgeon a sterile and ergonomically comfortable operation. A Microsoft Kinect 3D-sensor is used. The gesture control offers control of the level of illumination, the color temperature and functions of a camera for documentation requirements like zooming, rotating and taking a picture. A sophisticated procedure to logon and logoff the system prevents unintentional operation. The gesture control system and the conventional control panel were tested and compared in a usability study. The participating subjects rated the usability of the gesture control to be good to very good according to the System Usability Scale by Broke. 94% of the subjects perceived the gesture control to be ergonomically more comfortable. After simultaneous operation of the surgery light and execution of a cognitive task, 82% stated that the gesture control is less likely to distract them from that task. This data indicates that the concept of a gesture control with a 3D-sensor for a SLS is feasible and that it has a potential to receive a high degree of acceptance. EPiC Series in Computing Volume 41, 2016, Pages 138–146 GCAI 2016. 2nd Global Conference on Artificial Intelligence C.Benzmüller, G.Sutcliffe and R.Rojas (eds.), GCAI 2016 (EPiC Series in Computing, vol. 41), pp. 138–146